A Robust Face Recognition Method for Occluded and Low-Resolution Images

Ullah, Hidayat; Haq, Mahmood Ul; Khattak, Shadan; Khan, Gul Zameen; Mahmood, Zahid

doi:10.1109/icaem.2019.8853753

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…Future research on capsule networks should concentrate on overcoming existing constraints, expanding their applicability to diverse domains, and investigating fresh ways to enhance their performance, interpretability, and scalability for real-time problems such as object segmentation [121], biometric identification [122,123], and so on. In the upcoming years, there will be many opportunities for ground-breaking research and fascinating developments because the subject of capsule networks is so new.…”

Section: Capsnets In Generative Modelsmentioning

confidence: 99%

Capsule Network with Its Limitation, Modification, and Applications—A Survey

Haq

Sethi

Rehman

2023

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Numerous advancements in various fields, including pattern recognition and image classification, have been made thanks to modern computer vision and machine learning methods. The capsule network is one of the advanced machine learning algorithms that encodes features based on their hierarchical relationships. Basically, a capsule network is a type of neural network that performs inverse graphics to represent the object in different parts and view the existing relationship between these parts, unlike CNNs, which lose most of the evidence related to spatial location and requires lots of training data. So, we present a comparative review of various capsule network architectures used in various applications. The paper’s main contribution is that it summarizes and explains the significant current published capsule network architectures with their advantages, limitations, modifications, and applications.

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Section: Capsnets In Generative Modelsmentioning

confidence: 99%

Capsule Network with Its Limitation, Modification, and Applications—A Survey

Haq

Sethi

Rehman

2023

MAKE

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“…Face recognition technology is a subset of computer vision that focuses on identifying and verifying the identity of an individual based on their facial features [1]. The technology has been around since the 1960s, but it wasn't until the 1990s that significant progress was made in the field.…”

Section: Introductionmentioning

confidence: 99%

Automatic Player Face Detection and Recognition for Players in Cricket Games

Haq,

Sethi,

Ahmad

et al. 2024

IEEE Access

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In this Paper, we create an augmented reality cricket broadcasting application that uses player recognition and automatic detection during play to display player personal data. The system uses AdaBoost to detect player and player face and use PAL based face recognition model to recognize the faces of players on the field. The system is trained on a large dataset of cricket game footage and achieves high accuracy in detecting and recognizing players' faces even with several conditions such as occlusion, non-uniform illumination and pose variation. The system has the potential to enhance the viewing experience of cricket games by providing real-time player identification and statistics. The system can also be used in other sports to provide similar benefits. The paper discusses the system's methodology, results, and implications for the future of sports broadcasting. Overall, the system provides a promising solution for automatic player face detection and recognition in sports broadcasting.

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Coverless Steganography for Face Recognition Based on Diffusion Model

Guo,

Liu

2024

IEEE Access

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A Robust Face Recognition Method for Occluded and Low-Resolution Images

Cited by 7 publications

References 18 publications

Capsule Network with Its Limitation, Modification, and Applications—A Survey

Capsule Network with Its Limitation, Modification, and Applications—A Survey

Automatic Player Face Detection and Recognition for Players in Cricket Games

Coverless Steganography for Face Recognition Based on Diffusion Model

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